What Innovative Approaches to Analyzing Insurance Data Yield Unexpected Insights?

Analyze Commercial Auto Policy Data

While working at an insurance carrier, my team once searched commercial vehicle data to filter by elite car makes and models. We hadn't expected to find much and were surprised to discover that there were a number of commercial auto policies with elite luxury vehicles on them. From there, my team looked at the associated driver data and found a few policies where young drivers had been added to the commercial auto policy, usually with the same last name as the business owner. We were able to put our findings into a report for the commercial auto underwriting team so they could review them further and decide whether to take any action with their customers.

Utilize Predictive Analytics Models

We innovatively analyzed insurance data for a client by integrating predictive analytics models. These models uncovered unexpected patterns, particularly in the correlation between lifestyle changes and insurance needs. Armed with this insight, we adjusted marketing strategies to proactively target individuals undergoing such changes, leading to a significant increase in policy uptake within this demographic.

This application of predictive analytics not only provided unexpected insights but also demonstrated its value in anticipating customer needs. The refined marketing approach showcased the potential of leveraging advanced data analysis to optimize outcomes for our clients in the insurance industry.

Group Policies into Predictive Portfolios

When analyzing insurance data, specifically to predict losses, one of the techniques that provided unexpected results was the grouping of insurance policies into portfolios. These portfolios were samples based on various characteristics, such as risk metrics, demographics, etc. Creating portfolios and then generating high-level aggregated metrics allowed for increased accuracy when trying to predict things like the loss ratio in a particular portfolio.

Leverage Geo-Specific Pricing Data

At Rate Retriever, we use competitive pricing data to give consumers personalized rate estimates with geo-specific carrier results and pricing options. This has allowed us to learn which ZIP codes offer the highest opportunity for conversion for different insurance companies so that we can send them high-quality leads. The insight we glean from this is often unexpected, as a provider that has highly competitive pricing for a given profile in one ZIP code may be one of the most expensive options in another.